The Automated Screening Working Groups is a group of software engineers and biologists passionate about improving scientific manuscripts on a large scale. Our members have created tools that check for common problems in scientific manuscripts, including information needed to improve transparency and reproducibility. We have combined our tools into a single pipeline, called ScreenIT. We're currently using our tools to screen COVID preprints.
Latest preprint reviews
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The case for altruism in institutional diagnostic testing
This article has 13 authors:Reviewed by ScreenIT
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Outcomes of COVID-19: Disparities by ethnicity
This article has 13 authors:Reviewed by ScreenIT
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Contracting COVID-19: a longitudinal investigation of the impact of beliefs and knowledge
This article has 6 authors:Reviewed by ScreenIT
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Supporting COVID-19 policy response with large-scale mobility-based modeling
This article has 12 authors:Reviewed by ScreenIT
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Transmission of SARS-CoV-2 by children to contacts in schools and households: a prospective cohort and environmental sampling study in London
This article has 24 authors:Reviewed by Rapid Reviews Infectious Diseases, ScreenIT
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The impact of vaccination on the spread patterns of the COVID epidemic
This article has 2 authors:Reviewed by ScreenIT
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Practical Indicators for Risk of Airborne Transmission in Shared Indoor Environments and Their Application to COVID-19 Outbreaks
This article has 22 authors:Reviewed by ScreenIT
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County-level estimates of excess mortality associated with COVID-19 in the United States
This article has 6 authors:Reviewed by ScreenIT
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RT‐PCR/MALDI‐TOF mass spectrometry‐based detection of SARS‐CoV‐2 in saliva specimens
This article has 24 authors:Reviewed by ScreenIT
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PD-1 high CXCR5 – CD4 + Peripheral Helper T (Tph) cells Promote Tissue-Homing Plasmablasts in COVID-19
This article has 20 authors:Reviewed by ScreenIT